Import data

# --- customer ---
customer_raw <- fread("./data/marketing_campaign.csv")

alone_status <- c("Widow", "Single", "Divorced", "Alone", "Absurd")
customer <- customer_raw %>% select(-c(8, 27:28)) %>% 
  # for missing
  mutate(Income = ifelse(is.na(Income), round(mean(Income, na.rm = TRUE)), Income),
         Age = 2022 - Year_Birth,
         AcceptedComp = ifelse(rowSums(across(starts_with("AcceptedCmp"))) + Response > 0, 1, 0),
         Education = recode(Education, Basic = "Below some college", `2n Cycle` = "Associate degree",
                            Graduation = "Bachelor's degree", Master = "Master's degree", PhD = "Doctoral degree"),
         Numchild = Kidhome + Teenhome,
         MntTotal = MntWines + MntFruits + MntMeatProducts + MntFishProducts + MntSweetProducts + MntGoldProds,
         IsAlone = ifelse(Marital_Status %in% alone_status, 1, 0),
         FamilySize = ifelse(IsAlone == 1, 1, 2) + Numchild
         ) %>% 
  filter(Marital_Status != "YOLO") %>% 
  select(1, 27, 3, 31, 5, 29, 32, 8, 25, 9:15, 28, 16:19) %>% 
  mutate_at(c(3:4, 9, 17), .funs = ~as.factor(.))

# --- big five ---
# Import original data & sample 0.2%

# bf_dat <- fread("./data/data-final.csv")
# chose_ind <- sample(c(1:nrow(bf_dat)), size = round(nrow(bf_dat) * 0.002), replace = FALSE)
# bf <- bf_dat[chose_ind, ]
# write_csv(bf, "./data/big_five.csv")
big_five_raw <- fread("./data/big_five.csv")

country_df <- countrycode::codelist %>% 
  select(3, 6, 17)
colnames(country_df)[3] <- "country"
big_five_raw <- left_join(big_five_raw, country_df, by = "country") %>% 
  select(-c(51:104, 106, 108))
big_five_raw <- big_five_raw %>% 
  mutate(num_zero = rowSums(big_five_raw[, 1:50] == 0)) %>% 
  filter(num_zero == 0) %>% select(-57)

big_five <- big_five_raw %>% 
  filter(IPC == 1) %>% 
  mutate_at(c(1:50, 55:56), .funs = ~as.factor(.)) %>% 
  mutate(
    testlapse = as.numeric(testelapse),
    lat = as.numeric(lat_appx_lots_of_err),
    long = as.numeric(long_appx_lots_of_err),
    country = country.name.en
    ) %>% 
  select(-c(51:54, 56)) %>% 
  na.omit()

big_five <- big_five %>% mutate(id = seq(1:nrow(big_five))) %>% 
  relocate(id, .before = EXT1)

EDA

# --- customer ---
# Check correlation
check_cor <- model.matrix(~., customer)[, -1]
corrplot(cor(check_cor[, -1]), method = "circle", type = "full",
         tl.cex = 0.75, tl.col = "black")

summary(customer)
##        ID             Age                     Education    IsAlone 
##  Min.   :    0   Min.   : 26.0   Associate degree  : 203   0:1444  
##  1st Qu.: 2830   1st Qu.: 45.0   Bachelor's degree :1127   1: 794  
##  Median : 5458   Median : 52.0   Below some college:  54           
##  Mean   : 5592   Mean   : 53.2   Doctoral degree   : 484           
##  3rd Qu.: 8425   3rd Qu.: 63.0   Master's degree   : 370           
##  Max.   :11191   Max.   :129.0                                     
##      Income          Numchild        FamilySize       Recency      Complain
##  Min.   :  1730   Min.   :0.0000   Min.   :1.000   Min.   : 0.00   0:2217  
##  1st Qu.: 35528   1st Qu.:0.0000   1st Qu.:2.000   1st Qu.:24.00   1:  21  
##  Median : 51790   Median :1.0000   Median :3.000   Median :49.00           
##  Mean   : 52251   Mean   :0.9504   Mean   :2.596   Mean   :49.15           
##  3rd Qu.: 68307   3rd Qu.:1.0000   3rd Qu.:3.000   3rd Qu.:74.00           
##  Max.   :666666   Max.   :3.0000   Max.   :5.000   Max.   :99.00           
##     MntWines         MntFruits      MntMeatProducts  MntFishProducts 
##  Min.   :   0.00   Min.   :  0.00   Min.   :   0.0   Min.   :  0.00  
##  1st Qu.:  23.25   1st Qu.:  1.00   1st Qu.:  16.0   1st Qu.:  3.00  
##  Median : 173.00   Median :  8.00   Median :  67.0   Median : 12.00  
##  Mean   : 303.92   Mean   : 26.32   Mean   : 167.1   Mean   : 37.56  
##  3rd Qu.: 504.75   3rd Qu.: 33.00   3rd Qu.: 232.0   3rd Qu.: 50.00  
##  Max.   :1493.00   Max.   :199.00   Max.   :1725.0   Max.   :259.00  
##  MntSweetProducts  MntGoldProds    NumDealsPurchases AcceptedComp
##  Min.   :  0.00   Min.   :  0.00   Min.   : 0.000    0:1630      
##  1st Qu.:  1.00   1st Qu.:  9.00   1st Qu.: 1.000    1: 608      
##  Median :  8.00   Median : 24.00   Median : 2.000                
##  Mean   : 27.08   Mean   : 44.02   Mean   : 2.323                
##  3rd Qu.: 33.00   3rd Qu.: 56.00   3rd Qu.: 3.000                
##  Max.   :263.00   Max.   :362.00   Max.   :15.000                
##  NumWebPurchases  NumCatalogPurchases NumStorePurchases NumWebVisitsMonth
##  Min.   : 0.000   Min.   : 0.000      Min.   : 0.00     Min.   : 0.000   
##  1st Qu.: 2.000   1st Qu.: 0.000      1st Qu.: 3.00     1st Qu.: 3.000   
##  Median : 4.000   Median : 2.000      Median : 5.00     Median : 6.000   
##  Mean   : 4.082   Mean   : 2.664      Mean   : 5.79     Mean   : 5.314   
##  3rd Qu.: 6.000   3rd Qu.: 4.000      3rd Qu.: 8.00     3rd Qu.: 7.000   
##  Max.   :27.000   Max.   :28.000      Max.   :13.00     Max.   :20.000
# Total demographics
tbl_summary(dat = customer %>% 
              mutate(Complain = factor(Complain, levels = c(0, 1), labels = c("No", "Yes")),
                     IsAlone = factor(IsAlone, levels = c(0, 1), labels = c("No", "Yes")),
                     AcceptedComp = factor(AcceptedComp, levels = c(0, 1), labels = c("No", "Yes"))) %>% 
              select(-1),
            label = list(Numchild ~ "Number of Children", IsAlone ~ "Is Alone", FamilySize ~ "Family Size",
                         Recency ~ "Number of Days Since Last Purchase", Complain ~ "Complaint (in last 2 yrs)",
                         
                         # products
                         MntWines ~ "Wine", MntFruits ~ "Fruits", MntMeatProducts ~ "Meat Products",
                         MntFishProducts ~ "Fish Products", MntSweetProducts ~ "Sweet Products",
                         MntGoldProds ~ "Gold Products",
                         
                         # campaign & place
                         NumDealsPurchases ~ "Number of Purchases made with a discount", 
                         AcceptedComp ~ "Customer Accepted the Offer",
                         NumWebPurchases ~ "Through the Company's Website", NumCatalogPurchases ~ "Using a Catelogue",
                         NumStorePurchases ~ "Directly in Stores", NumWebVisitsMonth ~ "Website Visits (in last month)")) %>% 
  modify_table_styling(footnote = "Amount spent on a type of product in last two years",
                       rows = (label == "Wine"), columns = label) %>% 
  modify_table_styling(footnote = "Number of purchases made through a way",
                       rows = (label == "Through the Company's Website"),
                       columns = label) %>% 
  modify_caption("**Demographics of Customer (n=2238)**") %>% 
  bold_labels()
Demographics of Customer (n=2238)
Characteristic N = 2,2381
Age 52 (45, 63)
Education
    Associate degree 203 (9.1%)
    Bachelor's degree 1,127 (50%)
    Below some college 54 (2.4%)
    Doctoral degree 484 (22%)
    Master's degree 370 (17%)
Is Alone 794 (35%)
Income 51,790 (35,528, 68,307)
Number of Children
    0 638 (29%)
    1 1,126 (50%)
    2 421 (19%)
    3 53 (2.4%)
Family Size
    1 254 (11%)
    2 762 (34%)
    3 889 (40%)
    4 301 (13%)
    5 32 (1.4%)
Number of Days Since Last Purchase 49 (24, 74)
Complaint (in last 2 yrs) 21 (0.9%)
Wine2 173 (23, 505)
Fruits 8 (1, 33)
Meat Products 67 (16, 232)
Fish Products 12 (3, 50)
Sweet Products 8 (1, 33)
Gold Products 24 (9, 56)
Number of Purchases made with a discount 2 (1, 3)
Customer Accepted the Offer 608 (27%)
Through the Company's Website3 4 (2, 6)
Using a Catelogue 2 (0, 4)
Directly in Stores 5 (3, 8)
Website Visits (in last month) 6 (3, 7)
1 Median (IQR); n (%)
2 Amount spent on a type of product in last two years
3 Number of purchases made through a way
# --- Big Five ---
# Geographic map
mapworld <- borders("world", colour = "gray50", fill = "white")
big_five %>% ggplot() +
  geom_point(aes(x = long, y = lat, color = country), size = 1.5) + mapworld +
  theme_bw() + theme(legend.position = "none") +
  labs(x = "Longitude", y = "Latitude", title = "Geographic Distribution of Participants (n=1191)")

# Frequency by country
bf_eda <- big_five %>% 
  group_by(country) %>% summarise(tot = n())
bf_eda[order(bf_eda$tot, decreasing = TRUE), ] %>% 
  top_n(50) %>% 
  ggplot(aes(x = tot, y = reorder(country, tot))) +
  geom_bar(stat = "identity") + theme_bw() + 
  theme(legend.position = "none", axis.text.y = element_text(size = 7)) +
  labs(x = "Number of Participants", y = "Country", title = "Number of Participants by Country")

Clustering on customer data

K-means

set.seed(202212)
customer_con <- customer[, c(2, 5:8, 10:16, 18:21)]
customer_scale <- scale(customer_con)

fviz_nbclust(customer_scale, FUNcluster = kmeans, 
             method = "gap_stat", iter.max = 50)

km <- kmeans(customer_scale, centers = 5, nstart = 20)
fviz_cluster(list(data = customer_scale, cluster = km$cluster),
             ellipse.type = "convex", geom = "point",
             labelsize = 5, palette = "Dark2",
             main = "K-means Cluster Plot for Customer Data") + theme_bw()

K-medoids and Gower distance

# Gower distance
res_gower <- daisy(customer[, -1], metric = "gower")
summary(res_gower)
## 2503203 dissimilarities, summarized :
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.1612  0.2139  0.2162  0.2689  0.5479 
## Metric :  mixed ;  Types = I, N, N, I, I, I, I, N, I, I, I, I, I, I, I, N, I, I, I, I 
## Number of objects : 2238
# Find the optimal number of clusters - 3
res_silhouette <- lapply(2:10, function(x) {
  pam_clust <- pam(as.matrix(res_gower), diss = TRUE, k = x)
  silhouette <- pam_clust$silinfo$avg.width
})

do.call(rbind, res_silhouette) %>% 
  as.data.frame() %>% mutate(cluster = c(2:10)) %>% 
  ggplot(aes(x = cluster, y = V1)) +
  geom_line() + theme_bw() +
  labs(x = "Number of clusters k", y = "Sihouette Width", 
       title = "Optimal number of clusters")

km_mod <- pam(as.matrix(res_gower), diss = TRUE, k = 3)

# Clustering Comparison 
fossil::rand.index(km$cluster, km_mod$clustering)
## [1] 0.6208957
# --- Demographic for each cluster ---
tbl_summary(by = clust,
            data = customer %>% 
              mutate(clust = km_mod$clustering,
                     Complain = factor(Complain, levels = c(0, 1), labels = c("No", "Yes")),
                     IsAlone = factor(IsAlone, levels = c(0, 1), labels = c("No", "Yes")),
                     AcceptedComp = factor(AcceptedComp, levels = c(0, 1), labels = c("No", "Yes")),
                     clust = factor(clust, levels = c(1:3), labels = c("Cluster 1", "Cluster 2", "Cluster 3"))) %>% 
              select(-1),
            label = list(Numchild ~ "Number of Children", FamilySize ~ "Family Size",
                         IsAlone ~ "Is Alone", Recency ~ "Number of Days Since Last Purchase",
                         Complain ~ "Complaint (in last 2 yrs)",
                         
                         # products
                         MntWines ~ "Wine", MntFruits ~ "Fruits", MntMeatProducts ~ "Meat Products",
                         MntFishProducts ~ "Fish Products", MntSweetProducts ~ "Sweet Products",
                         MntGoldProds ~ "Gold Products", 
                         
                         # campaign & place
                         NumDealsPurchases ~ "Number of Purchases made with a discount", 
                         AcceptedComp ~ "Customer Accepted the Offer",
                         NumWebPurchases ~ "Through the Company's Website", NumCatalogPurchases ~ "Using a Catelogue",
                         NumStorePurchases ~ "Directly in Stores", NumWebVisitsMonth ~ "Website Visits (in last month)")) %>% 
  add_p(list(all_categorical() ~ "chisq.test", all_continuous() ~ "aov")) %>% add_overall() %>% 
  modify_table_styling(footnote = "Amount spent on a type of product in last two years",
                       rows = (label == "Wine"), columns = label) %>% 
  modify_table_styling(footnote = "Number of purchases made through a way",
                       rows = (label == "Through the Company's Website"),
                       columns = label) %>% 
  modify_caption("**Demographics of Customers for each K-medoids Cluster (n=2238)**") %>% 
  bold_labels()
Demographics of Customers for each K-medoids Cluster (n=2238)
Characteristic Overall, N = 2,2381 Cluster 1, N = 4461 Cluster 2, N = 6711 Cluster 3, N = 1,1211 p-value2
Age 52 (45, 63) 53 (43, 65) 52 (45, 62) 51 (45, 62) 0.2
Education 0.004
    Associate degree 203 (9.1%) 38 (8.5%) 53 (7.9%) 112 (10.0%)
    Bachelor's degree 1,127 (50%) 222 (50%) 353 (53%) 552 (49%)
    Below some college 54 (2.4%) 0 (0%) 20 (3.0%) 34 (3.0%)
    Doctoral degree 484 (22%) 115 (26%) 142 (21%) 227 (20%)
    Master's degree 370 (17%) 71 (16%) 103 (15%) 196 (17%)
Is Alone 794 (35%) 123 (28%) 671 (100%) 0 (0%) <0.001
Income 51,790 (35,528, 68,307) 77,040 (69,224, 82,783) 46,734 (33,347, 61,825) 44,155 (31,535, 57,937) <0.001
Number of Children <0.001
    0 638 (29%) 362 (81%) 141 (21%) 135 (12%)
    1 1,126 (50%) 74 (17%) 368 (55%) 684 (61%)
    2 421 (19%) 8 (1.8%) 141 (21%) 272 (24%)
    3 53 (2.4%) 2 (0.4%) 21 (3.1%) 30 (2.7%)
Family Size <0.001
    1 254 (11%) 113 (25%) 141 (21%) 0 (0%)
    2 762 (34%) 259 (58%) 368 (55%) 135 (12%)
    3 889 (40%) 64 (14%) 141 (21%) 684 (61%)
    4 301 (13%) 8 (1.8%) 21 (3.1%) 272 (24%)
    5 32 (1.4%) 2 (0.4%) 0 (0%) 30 (2.7%)
Number of Days Since Last Purchase 49 (24, 74) 42 (19, 71) 51 (27, 75) 50 (26, 75) 0.002
Complaint (in last 2 yrs) 21 (0.9%) 1 (0.2%) 7 (1.0%) 13 (1.2%) 0.2
Wine3 173 (23, 505) 710 (464, 960) 96 (17, 371) 73 (15, 292) <0.001
Fruits 8 (1, 33) 48 (23, 93) 6 (1, 21) 4 (1, 15) <0.001
Meat Products 67 (16, 232) 427 (258, 610) 44 (12, 142) 31 (12, 106) <0.001
Fish Products 12 (3, 50) 72 (33, 130) 8 (2, 31) 7 (2, 21) <0.001
Sweet Products 8 (1, 33) 49 (24, 96) 6 (1, 21) 5 (1, 16) <0.001
Gold Products 24 (9, 56) 57 (32, 114) 22 (8, 50) 16 (6, 40) <0.001
Number of Purchases made with a discount 2 (1, 3) 1 (1, 1) 2 (1, 3) 2 (1, 3) <0.001
Customer Accepted the Offer 608 (27%) 342 (77%) 124 (18%) 142 (13%) <0.001
Through the Company's Website4 4 (2, 6) 5 (4, 7) 3 (2, 5) 3 (2, 5) <0.001
Using a Catelogue 2 (0, 4) 6 (4, 7) 1 (0, 3) 1 (0, 2) <0.001
Directly in Stores 5 (3, 8) 8 (6, 11) 4 (3, 7) 4 (3, 7) <0.001
Website Visits (in last month) 6 (3, 7) 3 (2, 5) 6 (4, 7) 6 (5, 7) <0.001
1 Median (IQR); n (%)
2 One-way ANOVA; Pearson's Chi-squared test
3 Amount spent on a type of product in last two years
4 Number of purchases made through a way
# --- Heatmap ---
# Products
htp_1 <- customer %>% select(10:15) %>% 
  mutate(cluster = km_mod$clustering) %>% 
  pivot_longer(
    1:6, names_to = "product", values_to = "value"
  ) %>% 
  group_by(cluster, product) %>% 
  summarise(amount = mean(value)) %>% 
  ggplot(aes(x = cluster, y = product, fill = amount)) +
  geom_tile() + theme_bw() +
  theme(legend.position = "bottom") +
  scale_y_discrete(labels = c("Fish", "Fruit", "Gold", "Meat", "Sweet", "Wine")) +
  scale_fill_distiller(palette = "OrRd") +
  labs(x = "Cluster", y = "Product")

# Places
htp_2 <- customer %>% select(18:20) %>% 
  mutate(cluster = km_mod$clustering) %>% 
  pivot_longer(
    1:3, names_to = "place", values_to = "value"
  ) %>% 
  group_by(cluster, place) %>% 
  summarise(number = mean(value)) %>% 
  ggplot(aes(x = cluster, y = place, fill = number)) +
  geom_tile() + theme_bw() +
  theme(legend.position = "bottom") +
  scale_y_discrete(labels = c("Catalog", "Store", "Website")) +
  scale_fill_distiller(palette = "OrRd") +
  labs(x = "Cluster", y = "Place")

htp_1 + htp_2 +
  plot_annotation(title = "Heatmap of Amount Spend and Number of Visits by K-medoids Cluster")

# --- Networks for continuous vars: huge --- 
lapply(1:length(unique(km_mod$clustering)), function(x) {
  print(paste("Cluster", x, "with", sum(km$cluster == x), "subjects"))
  
  cl <- customer_con[km_mod$clustering == x,]
  net <- estimateNetwork(as.matrix(cl), default = "huge")
  qgraph(net$graph, labels = names(cl), layout = "spring",
         theme = "TeamFortress", label.cex = 1.5,
         label.fill.horizontal = .7)
})

Clustering on big five

Latent class analysis (LCA)

# Try #class from 2 to 8
lca_dat <- big_five[, 2:51] %>% mutate_all(.funs = ~as.numeric(.))
f <- formula(paste("cbind(", paste(colnames(lca_dat), collapse = ","), ") ~ 1"))

lca_res <- mclapply(2:8, function(x) {
  set.seed(202212)
  lca <- poLCA(f, lca_dat, nclass = x, verbose = FALSE, nrep = 20)
  return(lca)
}, mc.cores = num.cores)

df_bic <- data.frame(Class = 2:8, BIC = unlist(lapply(lca_res, function(obj) obj$bic)))
df_bic %>% 
  ggplot(aes(x = Class, y = BIC, label = round(BIC))) +
  geom_line() + geom_point() +
  geom_text(vjust = -.35) + theme_bw() +
  labs(title = "BIC for Latent Class Analysis for Big Five Data")

# Choose 5 classes with smallest BIC
n_class <- df_bic$Class[which.min(df_bic$BIC)]
fit_lca <- lca_res[[n_class - 1]]

Answers for each class

# --- Alluvial plots ---
# rename
score <- c("Excellent", "Very Good", "Good", "Fair", "Poor")
col_names <- c("PartyLife", "TalkLess", "SocialComfort", "Invisible", "TopicStarter", 
               "SayLittle", "ExtensiveContact", "AvoidAttention", "CentralPerson", "Quiet",
               "Stressed", "Relaxed", "Worried", "FeelBlueLess", "Distubed",
               "GetUpset", "MoodChange", "FreqMoodSwing", "Irritated", "FeelBlue",
               "ConcernLess", "Interested", "Insult", "Sympathize", "NotInterestProb",
               "SoftHeart", "NotInterestPpl", "TakeTime", "FeelEmo", "MakeEase",
               "Prepared", "LeaveBelonging", "Meticulous", "MakeMess", "NoDelay",
               "Forgetful", "LikeOrder", "ShirkDuty", "FollowSche", "Exacting",
               "RichVocal", "DiffAbstract", "ViviImag", "NoInAbstract", "ExcelIdea",
               "NoImag", "QuickUnderstand", "DiffWord", "SlowReflect", "FullIdea")
five_factor <- data.frame(
  fact_name = c("Extraversion", "EmoStability", "Agreeableness", "Conscientious", "Openness"),
  factr = c("EXT", "EST", "AGR", "CSN", "OPN"))
# set pos or neg answers
dirct <- data.frame(variable = c(paste("EXT", seq(10), sep = ""), 
                                 paste("EST", seq(10), sep = ""),
                                 paste("AGR", seq(10), sep = ""),
                                 paste("CSN", seq(10), sep = ""),
                                 paste("OPN", seq(10), sep = "")),
                    dirct = c(rep(c(1, 0), 5),
                              0, 1, 0, 1, rep(0, 6),
                              0, 1, 0, 1, 0, 1, 0, 1, 1, 1,
                              1, 0, 1, 0, 1, 0, 1, 0, 1, 1,
                              1, 0, 1, 0, 1, 0, 1, 1, 1, 1))
# combine lca clusters
dat_clust <- lca_dat %>% 
  mutate(clust = fit_lca$predclass)

dat_forshow <- dat_clust %>% 
  pivot_longer(1:50, names_to = "variable", values_to = "value") %>% 
  group_by(variable, clust, value) %>% 
  summarise(tot = n()) %>% 
  pivot_wider(c(1:2), names_from = "value", values_from = "tot")
dat_forshow[is.na(dat_forshow)] <- 0

# change frequency to percentage
# convert answer to score: subtract 6 by answers of negative variables
tot_dat <- dat_forshow %>% 
  pivot_longer(3:7, names_to = "answer", values_to = "number") %>% 
  group_by(variable, answer) %>% summarise(tot = sum(number)) %>% 
  mutate(comb = paste(variable, answer, sep = ""))
dat_forshow_1 <- dat_forshow %>% 
  pivot_longer(3:7, names_to = "answer", values_to = "number") %>% 
  mutate(comb = paste(variable, answer, sep = ""))
dat_forshow_1 <- left_join(dat_forshow_1, dirct, by = "variable")
dat_forshow_2 <- dat_forshow_1 %>% 
  mutate(
    answer = as.numeric(answer),
    score = ifelse(dirct == 0, 6 - answer, answer)) %>% 
  select(1, 2, 7, 4, 5)

# change label name
dat_forshow_3 <- left_join(dat_forshow_2, 
                           tot_dat %>% select(3, 4), by = "comb") %>% 
  mutate(variable = variable.x, prct = number/tot * 100) %>% 
  select(8, 2:3, 9) %>% pivot_wider(names_from = "score", values_from = "prct")

# change factor name
dat_forshow_4 <- dat_forshow_3 %>% 
  mutate(factr = substr(variable, 1, 3))
dat_forshow_5 <- 
  left_join(dat_forshow_4, 
            data.frame(name = col_names, variable = c(paste("EXT", seq(10), sep = ""), 
                                                      paste("EST", seq(10), sep = ""),
                                                      paste("AGR", seq(10), sep = ""),
                                                      paste("CSN", seq(10), sep = ""),
                                                      paste("OPN", seq(10), sep = ""))),
            by = "variable")

dat_forshow_fin <- left_join(dat_forshow_5, five_factor, by = "factr")
make_alluvial <- function(x){
  dat_forshow_fin[, -c(1, 8)] %>% 
    pivot_longer(c(2:6), names_to = "Score", values_to = "Prct") %>% 
    mutate(clust = as.factor(clust),
           Score = factor(Score, levels = c(5:1), labels = score)) %>% 
    filter(str_detect(fact_name, x)) %>% 
    ggplot(aes(y = Prct, axis1 = name, axis2 = Score, axis3 = clust)) +
    geom_alluvium(aes(fill = clust), width = 1/12) +
    geom_stratum(width = 1/12, fill = "black", color = "grey") +
    geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
    scale_x_discrete(limits = c("Question", "Score", "Cluster"), expand = c(.09, .09)) +
    scale_y_continuous(breaks = NULL) +
    scale_fill_brewer(type = "qual", palette = "Dark2") +
    guides(fill = guide_legend(title = "Cluster")) +
    theme_bw() +
    labs(y = "Percentage",
         title = paste(x))
}

# plot alluvial
lapply(unique(dat_forshow_fin$fact_name), function(x){
  make_alluvial(x)})

# combine plots
plt1 <- make_alluvial("Agreeableness")
plt2 <- make_alluvial("Conscientious")
plt3 <- make_alluvial("EmoStability")
plt4 <- make_alluvial("Extraversion")
plt5 <- make_alluvial("Openness")

ggarrange(plt1, plt2, plt3, plt4, plt5, ncol = 3, nrow = 2, common.legend = TRUE, legend = "bottom") %>% 
  annotate_figure(top = text_grob("Alluvial Plots"))

Factor-lead subgroups

# --- cluster 4: excellent on all five ---
clust_4 <- big_five %>% 
  mutate(clust = fit_lca$predclass) %>% 
  filter(clust == 4)

# by continent
clust_4 %>% 
  group_by(continent) %>% summarise(tot = n()) %>% 
  mutate(prct = tot/sum(fit_lca$predclass == 4) * 100) %>% 
  ggplot(aes(x = prct, y = continent)) +
  geom_bar(stat = "identity")

# --- Cluster 5: most worst group---
clust_5 <- big_five %>% 
  mutate(clust = fit_lca$predclass) %>% 
  filter(clust == 5)

clust_5 %>% 
  group_by(continent) %>% summarise(tot = n()) %>% 
  mutate(prct = tot/sum(fit_lca$predclass == 5) * 100) %>% 
  ggplot(aes(x = prct, y = continent)) +
  geom_bar(stat = "identity")